gradDescent: Gradient Descent for Regression Tasks

An implementation of various learning algorithms based on Gradient Descent for dealing with regression tasks.
The variants of gradient descent algorithm are :
Mini-Batch Gradient Descent (MBGD), which is an optimization to use training data partially to reduce the computation load.
Stochastic Gradient Descent (SGD), which is an optimization to use a random data in learning to reduce the computation load drastically.
Stochastic Average Gradient (SAG), which is a SGD-based algorithm to minimize stochastic step to average.
Momentum Gradient Descent (MGD), which is an optimization to speed-up gradient descent learning.
Accelerated Gradient Descent (AGD), which is an optimization to accelerate gradient descent learning.
Adagrad, which is a gradient-descent-based algorithm that accumulate previous cost to do adaptive learning.
Adadelta, which is a gradient-descent-based algorithm that use hessian approximation to do adaptive learning.
RMSprop, which is a gradient-descent-based algorithm that combine Adagrad and Adadelta adaptive learning ability.
Adam, which is a gradient-descent-based algorithm that mean and variance moment to do adaptive learning.